The present invention relates to a technique for determining a translation equivalent to a word or a phrase to be translated to different words or phrases in image data. In particular, the present invention relates to a method for determining a suitable translation equivalent to a word or a phrase from among a plurality of translation candidates.
In the case where one word is to be translated to another language, if there are a plurality of translation equivalents to the one word, it is appropriate to translate the one word into different words depending on the context, situation, and so on. When performing the above one-to-many translation, the translator needs to select a suitable translation equivalent to the one word with reference to a sentence including the one word, with reference to peripheral terms within about upper and lower ten lines including the one word, or with reference to a screen on which the one word is displayed.
In the case where a translator translates a screen of computer software that uses a graphical user interface (hereinafter also referred to as a GUI) or a document including the screen (hereinafter referred to as a GUI image document) or in the case where a client asks an outside translator to translate the GUI image document, the translator or the client sometimes list specific words in the GUI image document in advance and determine translation equivalents to the specific words from among a plurality of words. However, the specific words are sometimes sorted in alphabetical order in the list. In the case where the specific words are sorted in alphabetical order, the relationship between the specific words and peripheral terms thereof is lost, which makes it difficult for the translator or the client to perform one-to-many translation of the specific words.
A method in which translation software selects a suitable translation equivalent is roughly divided into, for example, a method of one-to-many translation based on semantic information on the subject, object, and predicate and a method of one-to-many translation depending on the field of a sentence to be translated. For example, the former method is effective in translating novels and letters, and the latter method is effective in translating manuals and scientific and technical sentences. Since the main targets of existing translation software are manuals and scientific and technical sentences, the latter method undergoes further improvements to improve the quality of translations. However, in the case where translation software translates a specific word in the GUI image document, it is still difficult to translate the specific word to suitable different words by the present methods.
In the prior art, a machine translation system executes translation processing using a computer, a suitable-translation-equivalent selection method for selecting the most suitable translation equivalent from a plurality of translation equivalent candidates for a word Tj in an input original, if present. That is, prior art discloses that the machine translation system comprises a co-occurrence dictionary and a suitable-translation-equivalent selection unit that executes a suitable-translation-equivalent selection process for selecting a suitable translation equivalent from a plurality of translation candidates by using the co-occurrence dictionary. The co-occurrence dictionary is configured such that words that appear together with each of entry words in a source language are set as co-occurring words, and an evaluation value K indicates the degree of appearance of each entry word together with each co-occurring word is described for each co-occurring word. In the suitable-translation-equivalent selection process of the suitable-translation-equivalent selection unit, co-occurring words for each translation candidate are looked up by using the co-occurrence dictionary. It is determined whether a co-occurrence-related word, in which the co-occurring word itself is a translation equivalent candidate, is present in the original word that is to be translated. If a co-occurrence-related word is present, an evaluation value Vi indicating the degree of use of the translation candidate as a suitable translation equivalent is calculated for each translation candidate, in consideration of the position and the number of times of appearance of the co-occurrence-related word for the word Tj in the original, for which a suitable translation equivalent is to be given, and the evaluation value K of the co-occurring word in the co-occurrence dictionary, and a translation candidate i whose evaluation value Vi is the largest is selected as a suitable translation equivalent to the word Tj from the plurality of translation candidates for the word Tj in the original.
Other prior art discloses a machine translator that performs natural language processing. Such prior art teaches a machine translator that comprises translation-target-area-selection-information storage means that stores information for selecting a translation target area in a document in a source language and translation-target-area determination means that determines a translation target area on the basis of the information stored in the storage means; a translation result can be quickly presented by translating only a specific area determined on the basis of the information serving as a key for selecting the translation target area.
Still yet other prior art teaches a machine translator and a computer-readable storage medium in which a machine-translation processing program is stored. In particular, such other prior art discloses a technique for appropriately translating a sentence expressed using an idiomatic expression and a sentence other than that in different manner.
Still yet other prior art teaches a machine translator, and in particular, a machine translator that translates a fixed-form sentence by using a method of searching a table in which translations corresponding to such fixed-form sentences are stored.
Still yet other prior art teaches a machine translator that translates a first language to a second language by using both normal translation processing using a translation dictionary and fixed-form-sentence processing using a fixed-form sentence dictionary.
Still yet other prior art teaches a natural language translator that translates natural languages using a computer.
Still yet other prior art teaches an apparatus, method, and program for supporting communication between persons who speak in different languages.
Still yet other prior art teaches a natural language analysis unit, and in particular, it discloses a natural language analysis unit that performs machine translation, such as natural language analysis processing.
Still yet other prior art teaches a machine translator and a machine translation program for translating sentences in a first language, such as English, to sentences in a second language, such as Japanese. Such other prior art also provides a machine translator and a machine translation program in which the possibility that words in a character string obtained by keying an original or capturing a Web page with a scanner are detected as unknown words or ungrammatical sentences is reduced when machine-translated, and thus the translation accuracy and operating efficiency can be improved.
Still yet other prior art teaches an information processing unit that, when receiving a keyword as an input, outputs a content corresponding to the keyword, and a method for the same, and in particular, it discloses an information processing unit, such as an electronic dictionary or a translator that displays an example sentence and a translation thereof, and an information processing method using the information processing unit.
Still yet other prior art teaches a machine-translation support unit that supports post-processing of the translation result of a machine translator and a method for the same.
Still yet other prior art teaches a natural-language recognition process, and in particular, it discloses a natural-language processing unit that executes a process corresponding to the semantic content of a sentence in a natural language, and a method and program for the same.